The accuracy of multivariate time series forecasting has been largely improved by artificial intelligence [1]}, especially deep learning techniques.
Among these techniques, Spatial-Temporal Graph Neural Networks (STGNNs) are the most promising methods,
which combine Graph Neural Networks (GNNs) [2]}, [3]} and sequential models [4]}, [5]} to model the spatial and temporal dependency jointly.
Graph WaveNet [6]}, MTGNN [7]}, STGCN [8]}, and StemGNN [9]} combine graph convolutional networks and gated temporal convolutional networks with their variants. These methods are based on convolution operation, which facilitates parallel computation.
DCRNN [10]}, ST-MetaNet [11]}, AGCRN [12]}, and TGCN [13]} combine diffusion convolutional networks and recurrent neural networks [4]}, [5]} with their variants.
They follow the seq2seq [5]} architecture to predict step by step.
Moreover, attention mechanism is widely used in many methods, such as GMAN [17]} and ASTGCN [18]}.
Although STGNNs have made significant progress, the complexity of STGNNs is high because it needs to deal with both temporal and spatial dependency at every step.
Therefore, STGNNs can only take short-term historical time series as input, such as the past 1 hour (twelve time steps in many datasets).
